Fixed-wing Unmanned Aerial Systems Research Articles

A path planning algorithm for a crop monitoring fixed-wing unmanned aerial system

A path planning algorithm for a crop monitoring fixed-wing unmanned aerial system

Precision Post-Stall Landing Using NMPC With Learned Aerodynamics

In this paper, we present an approach for achieving precision post-stall landings with medium-sized Group 1 Unmanned Aerial Systems (UAS). To do this, we employ an aggressive dive-and-stall maneuver to significantly reduce maneuver distance, time, and touchdown speed. Our approach relies on a nonlinear model predictive control (NMPC) algorithm and learned aerodynamic coefficients to achieve accuracy and reliability in the presence of wind disturbances. We demonstrate our approach in hardware with a 60-inch wingspan, 4.2 kg fixed-wing UAS, and show the ability to land with low speed and high accuracy using minimal throttle.

Land Cover Classification from Very High-Resolution UAS Data for Flood Risk Mapping.

Monitoring the world’s areas that are more vulnerable to natural hazards has become crucial worldwide. In order to reduce disaster risk, effective tools and relevant land cover (LC) data are needed. This work aimed to generate a high-resolution LC map of flood-prone rural villages in southwest Niger using multispectral drone imagery. The LC was focused on highly thematically detailed classes. Two photogrammetric flights of fixed-wing unmanned aerial systems (UAS) using RGB and NIR optical sensors were realized. The LC input dataset was generated using structure from motion (SfM) standard workflow, resulting in two orthomosaics and a digital surface model (DSM). The LC system is composed of nine classes, which are relevant for estimating flood-induced potential damages, such as houses and production areas. The LC was generated through object-oriented supervised classification using a random forest (RF) classifier. Textural and elevation features were computed to overcome the mapping difficulties due to the high spectral homogeneity of cover types. The training-test dataset was manually defined. The segmentation resulted in an F1_score of 0.70 and a median Jaccard index of 0.88. The RF model performed with an overall accuracy of 0.94, with the grasslands and the rocky clustered areas classes the least performant.

BOREAL—A Fixed-Wing Unmanned Aerial System for the Measurement of Wind and Turbulence in the Atmospheric Boundary Layer

Abstract An instrumentation package for wind and turbulence observations in the atmospheric boundary layer on an unmanned aerial vehicle (UAV) called BOREAL has been developed. BOREAL is a fixed-wing UAV built by BOREAL company, which weighs up to 25 kg (5 kg of payload) and has a wingspan of 4.2 m. With a light payload and optimal weather conditions, it has a flight endurance of 9 h. The instrumental payload was designed in order to measure every parameter required for the computation of the three wind components, at a rate of 100 s−1, which is fast enough to capture turbulence fluctuations: a GPS–inertial measurement unit (IMU) platform measures the three components of the groundspeed a well as the attitude angles; the airplane nose has been replaced by a five-hole probe in order to measure the angles of attack and sideslip, according to the so-called radome technique. This probe was calibrated using computational fluid dynamics (CFD) simulations and wind tunnel tests. The remaining instruments are a Pitot tube for static and dynamic pressure measurement and temperature/humidity sensors in dedicated housings. The optimal airspeed at which the vibrations are significantly reduced to an acceptable level was defined from qualification flights. With appropriate flight patterns, the reliability of the mean wind estimates, through self-consistency and comparison with observations performed at 60 m on an instrumented tower could be assessed. Promising first observations of turbulence up to frequencies around 10 Hz and corresponding to a spatial resolution to the order of 3 m are hereby presented.

Development and validation of finite element impact models of high-density UAS components for use in air-to-air collision simulations

Understanding the severity of mid-air collisions between lightweight fixed-wing unmanned aerial systems (FW-UASs) and manned aircraft is a critical flight safety issue. Finite element models of high-density FW-UAS components were developed and validated; such components are particularly damaging during air-to-air collisions. FW-UAS motors and batteries were launched into semi-monocoque 2024-T3 targets using a compressed gas gun. Material/geometric nonlinear simulations of each impact were performed and correlated with physical test results. Predicted skin penetrations/perforations, projectile residual velocities, strain-time histories, and target forces matched experimental observations. Validated component models will be integrated into high-fidelity air-to-air collision simulations involving FW-UASs and manned aircraft.

Identification and Analysis of Microscale Hydrologic Flood Impacts Using Unmanned Aerial Systems

The need for accurate and spatially detailed hydrologic information is critical due to the microscale influences on the severity and distribution of flooding, and new and/or updated approaches in observations of river systems are required that are in line with the current push towards microscale numerical simulations. In response, the aim of this project is to define and illustrate the hydrologic response of river flooding relative to microscale surface properties by using an unmanned aerial system (UAS) with dedicated imaging, sensor, and communication packages for data collection. As part of a larger project focused on increasing situational awareness during flood events, a fixed-wing UAS was used to overfly areas near Greenwood, MS before and during a flood event in February 2019 to provide high-resolution visible and infrared imagery for analysis of hydrologic features. The imagery obtained from these missions provide direct examples of fine-scale surface features that can alter water level and discharge, such as built structures (i.e., levees and bridges), natural storage features (low-lying agricultural fields), and areas of natural resistance (inundated forests). This type of information is critical in defining where and how to incorporate high-resolution information into hydrologic models and also provides an invaluable dataset for eventual verification of hydrologic simulations through inundation mapping.

Autonomous ballistic airdrop of objects from a small fixed-wing unmanned aerial vehicle

Autonomous airdrop is a useful basic operation for a fixed-wing unmanned aerial system. Being able to deliver an object to a known target position extends operational range without risking human lives, but is still limited to known delivery locations. If the fixed-wing unmanned aerial vehicle delivering the object could also recognize its target, the system would take one step further in the direction of autonomy. This paper presents a closed-loop autonomous delivery system that uses machine vision to identify a target marked with a distinct colour, calculates the geographical coordinates of the target location and plans a path to a release point, where it delivers the object. Experimental results present a visual target estimator with a mean error distance of 3.4 m and objects delivered with a mean error distance of 5.5 m.

Estimating and Examining the Sensitivity of Different Vegetation Indices to Fractions of Vegetation Cover at Different Scaling Grids for Early Stage Acacia Plantation Forests Using a Fixed-Wing UAS

Understanding the information on land conditions and especially green vegetation cover is important for monitoring ecosystem dynamics. The fraction of vegetation cover (FVC) is a key variable that can be used to observe vegetation cover trends. Conventionally, satellite data are utilized to compute these variables, although computations in regions such as the tropics can limit the amount of available observation information due to frequent cloud coverage. Unmanned aerial systems (UASs) have become increasingly prominent in recent research and can remotely sense using the same methods as satellites but at a lower altitude. UASs are not limited by clouds and have a much higher resolution. This study utilizes a UAS to determine the emerging trends for FVC estimates at an industrial plantation site in Indonesia, which utilizes fast-growing Acacia trees that can rapidly change the land conditions. First, the UAS was utilized to collect high-resolution RGB imagery and multispectral images for the study area. The data were used to develop general land use/land cover (LULC) information for the site. Multispectral data were converted to various vegetation indices, and within the determined resolution grid (5, 10, 30 and 60 m), the fraction of each LULC type was analyzed for its correlation between the different vegetation indices (Vis). Finally, a simple empirical model was developed to estimate the FVC from the UAS data. The results show the correlation between the FVC (acacias) and different Vis ranging from R2 = 0.66–0.74, 0.76–0.8, 0.84–0.89 and 0.93–0.94 for 5, 10, 30 and 60 m grid resolutions, respectively. This study indicates that UAS-based FVC estimations can be used for observing fast-growing acacia trees at a fine scale resolution, which may assist current restoration programs in Indonesia.

Control of Multi-Agent Collaborative Fixed-Wing UASs in Unstructured Environment

In recent years, the study of dynamics and control of swarming robots and aircraft has been an active research topic. Many multi-agent collaborative control algorithms have been developed and have been validated in simulations, however the technological and logistic complexity involved in validation of these algorithms in actual flight tests has been a major hurdle impeding more frequent and wider applications. This work presents robust navigation algorithms for multi-agent fixed-wing aircraft based on an adaptive moving mesh partial differential equations controlled by the free energy heat flow equation. Guidance, navigation, and control algorithms for control of multi-agent unmanned aerial system (UASs) were validated through actual flight tests, and the robustness of these algorithms were also investigated using different aircraft platforms. The verification and validation flight tests were conducted using two different fixed-wing platforms: A DG808 sail-plane with a 4m wingspan T-tail configuration and a Skyhunter aircraft utilizing a 2.4m wingspan and a twin-boom configuration. The developed swarm navigation algorithm uses a virtual leader guidance scheme and has been implemented and optimized using optimal control theory. Multi-scale moving point guidance has been developed and complimented by a linear quadratic regulator controller. Several flight tests have been successfully conducted and a system of systems including software and hardware was successfully validated and verified.

Fast 3D Collision Avoidance Algorithm for Fixed Wing UAS

This paper presents an efficient 3D collision avoidance algorithm for fixed wing Unmanned Aerial Systems (UAS). The algorithm increases the ability of aircraft operations to complete mission goals by enabling fast collision avoidance of multiple obstacles. The new algorithm, which we have named Fast Geometric Avoidance algorithm (FGA), combines geometric avoidance of obstacles and selection of a critical avoidance start time based on kinematic considerations, collision likelihood, and navigation constraints. In comparison to a current way-point generation method, FGA showed a 90% of reduction in computational time for the same obstacle avoidance scenario. Using this algorithm, the UAS is able to avoid static and dynamic obstacles while still being able to recover its original trajectory after successful collision avoidance. Simulations for different mission scenarios show that this method is much more efficient at avoiding multiple obstacles than previous methods. Algorithm effectiveness validation is provided with Monte Carlo simulations and flight missions in an aircraft simulator. FGA was also tested on a fixed-wing aircraft with successful results. Because this algorithm does not have specific requirements on the sensor data types it can be applied to cooperative and non-cooperative intruders.

Distributed Pressure Sensing–Based Flight Control for Small Fixed-Wing Unmanned Aerial Systems

Small fixed-wing unmanned aerial systems (UAS) may require increased agility when operating in turbulent wind fields. In these conditions, conventional sensor suites could be augmented with additional flow-sensing to extend the aircraft’s usable flight envelope. Inspired by distributed sensor arrays in biological systems, a UAS with a chordwise array of pressure sensors was developed. Wind-tunnel testing characterized these sensors alongside a conventional airspeed sensor and an angle-of-attack (AoA) vane, and showed a single pressure measurement gave a linear response to AoA prestall. Flight tests initially manually piloted the vehicle through pitching maneuvers, then in a series of automated maneuvers based on closed-loop feedback using an estimate of AoA from the single pressure port. The AoA estimate was successfully used to control the attitude of the aircraft. An artificial neural network (ANN) was trained to estimate the AoA and airspeed using all pressure ports in the array, and validated using flight-trial data. The ANN more accurately estimated the AoA over a single-port method with good robustness to stall and unsteady flow. Distributed flow sensors could be used to supplement conventional flight control systems, providing enhanced information about wing flow conditions with application to systems with highly flexible or morphing wings.

Calibration Procedure and Accuracy of Wind and Turbulence Measurements with Five-Hole Probes on Fixed-Wing Unmanned Aircraft in the Atmospheric Boundary Layer and Wind Turbine Wakes

For research in the atmospheric boundary layer and in the vicinity of wind turbines, the turbulent 3D wind vector can be measured from fixed-wing unmanned aerial systems (UAS) with a five-hole probe and an inertial navigation system. Since non-zero vertical wind and varying horizontal wind causes variations in the airspeed of the UAS, and since it is desirable to sample with a flexible cruising airspeed to match a broad range of operational requirements, the influence of airspeed variations on mean values and turbulence statistics is investigated. Three calibrations of the five-hole probe at three different airspeeds are applied to the data of three flight experiments. Mean values and statistical moments of second order, calculated from horizontal straight level flights are compared between flights in a stably stratified polar boundary layer and flights over complex terrain in high turbulence. Mean values are robust against airspeed variations, but the turbulent kinetic energy, variances and especially covariances, and the integral length scale are strongly influenced. Furthermore, a transect through the wake of a wind turbine and a tip vortex is analyzed, showing the instantaneous influence of the intense variations of the airspeed on the measurement of the turbulent 3D wind vector. For turbulence statistics, flux calculations, and quantitative analysis of turbine wake characteristics, an independent measurement of the true airspeed with a pitot tube and the interpolation of calibration polynomials at different Reynolds numbers of the probe’s tip onto the Reynolds number during the measurement, reducing the uncertainty significantly.

Evaluating the Effectiveness of Unmanned Aerial Systems (UAS) for Collecting Thematic Map Accuracy Assessment Reference Data in New England Forests

Thematic mapping provides today’s analysts with an essential geospatial science tool for conveying spatial information. The advancement of remote sensing and computer science technologies has provided classification methods for mapping at both pixel-based and object-based analysis, for increasingly complex environments. These thematic maps then serve as vital resources for a variety of research and management needs. However, to properly use the resulting thematic map as a decision-making support tool, an assessment of map accuracy must be performed. The methods for assessing thematic accuracy have coalesced into a site-specific multivariate analysis of error, measuring uncertainty in relation to an established reality known as reference data. Ensuring statistical validity, access and time constraints, and immense costs limit the collection of reference data in many projects. Therefore, this research proposes evaluating the feasibility of adopting the low-cost, flexible, high-resolution sensor-capable Unmanned Aerial Systems (UAS, UAV, or Drone) platform for collecting reference data to use in thematic map accuracy assessments for complex environments. This pilot study analyzed 377.57 ha of New England forests, over six University of New Hampshire woodland properties, to compare the similarity between UAS-derived orthomosaic samples and ground-based continuous forest inventory (CFI) plot classifications of deciduous, mixed, and coniferous forest cover types. Using an eBee Plus fixed-wing UAS, 9173 images were acquired and used to create six comprehensive orthomosaics. Agreement between our UAS orthomosaics and ground-based sampling forest compositions reached 71.43% for pixel-based classification and 85.71% for object-based classification reference data methods. Despite several documented sources of uncertainty or error, this research demonstrated that UAS are capable of highly efficient and effective thematic map accuracy assessment reference data collection. As UAS hardware, software, and implementation policies continue to evolve, the potential to meet the challenges of accurate and timely reference data collection will only increase.

The Use of Unmanned Aerial Systems to Map Intertidal Sediment

This paper describes a new methodology to map intertidal sediment using a commercially available unmanned aerial system (UAS). A fixed-wing UAS was flown with both thermal and multispectral cameras over three study sites comprising of sandy and muddy areas. Thermal signatures of sediment type were not observable in the recorded data and therefore only the multispectral results were used in the sediment classification. The multispectral camera consisted of a Red–Green–Blue (RGB) camera and four multispectral sensors covering the green, red, red edge and near-infrared bands. Statistically significant correlations (>99%) were noted between the multispectral reflectance and both moisture content and median grain size. The best correlation against median grain size was found with the near-infrared band. Three classification methodologies were tested to split the intertidal area into sand and mud: k-means clustering, artificial neural networks, and the random forest approach. Classification methodologies were tested with nine input subsets of the available data channels, including transforming the RGB colorspace to the Hue–Saturation–Value (HSV) colorspace. The classification approach that gave the best performance, based on the j-index, was when an artificial neural network was utilized with near-infrared reflectance and HSV color as input data. Classification performance ranged from good to excellent, with values of Youden’s j-index ranging from 0.6 to 0.97 depending on flight date and site.

Estimation of plant health in a sorghum field infected with anthracnose using a fixed-wing unmanned aerial system

ABSTRACT Diseases cause enormous losses of yield and quality for crop producers worldwide. To meet future food demands, crops are bred for resistance to as many of these maladies as possible. One such disease, anthracnose [Colletotrichum sublineola], is a fungal disease of great importance to sorghum [Sorghum bicolor, L. Moench] production because it causes significant annual economic losses in the crop. Breeding for anthracnose resistance requires time-consuming phenotyping, which is subjective and conditional to the evaluator. It is possible that quantitative assessment using high-throughput methodologies to estimate the trait may be more effective. In this study, we present an in-depth statistical analysis of fixed-wing, unmanned aerial system (UAS) evaluation of anthracnose incidence and severity in sorghum using normalized difference vegetation index (NDVI). In early phases of infection, correlations between ground-truth and UAS estimates of anthracnose were moderate but they increased substantially by the end of the season (r = −0.55 to −0.95). Additionally, both metrics had moderate-to-high repeatabilities throughout the growth period (R = 0.60–0.90), indicating they were consistently able to differentiate genotypes. Finally, we found that the UAS-derived measurements (R 2 = 0.377, 0.473) were better associated with ground-truth measurements (R 2 = 0.278, 0.347) for grain yield under anthracnose pressure. The results of this study indicated that fixed-wing UAS could potentially be effective for evaluating anthracnose disease present in sorghum, and the greater range of the UAS allowed the effective evaluation of larger numbers of plants than ground truth or traditional remote sensing methods.

The potential of Unmanned Aerial Systems: A tool towards precision classification of hard-to-distinguish vegetation types?

Detail plant species classification using very high spatial resolution data is a challenging task. Exploring the potential of imagery acquired by Unmanned Aerial Vehicle (UAV) to identify individual species of plants and assessing values of additional inputs such as height and thermal information into classification process are hot research topics. Our study uses a fusion of visible, multispectral and thermal imagery acquired through the low altitude aerial survey for detail classification of land cover and vegetation types. The study area is located in the central part of the Czech Republic and situated in an environmentally specific area – an arboretum of 2.45 ha. Visible (i.e. RGB), multispectral, and thermal sensors were mounted on a flying fixed-wing Unmanned Aerial System. The imagery was acquired at a very detailed scale with Ground Sampling Distance of 3–18 cm. Besides three mosaics (one from each sensor), normalized Digital Surface Models were built from visible and multispectral sensors. Eight classification models were created – each mosaic (visible/multispectral) was enriched with height data, thermal data, and combined height and thermal information. A classification into a three level system was performed through Geographic Object-based Image Analysis using Support Vector Machine algorithm. In general, Overall Accuracy grew with the amount of information entering the classification process. Accuracy reached 77 – 91 % depending on the level of generalization for the best model based on multispectral data and 67 – 80 % for data from the visible sensor. Both thermal data and height information improved the accuracy; however, the statistical evaluation did not reveal any significant difference between the contribution of height and thermal data. Results also indicate that increasing spectral resolution leads to a significantly better performance of the models than higher spatial resolution. UAVs equipped with a proper sensor provide a convenient technology for detail land cover classification even in areas with many similar plant species.

An Alternative Parity Space-Based Fault Diagnosability Analysis Approach for Linear Discrete Time Systems

This paper deals with the problem of parity space-based fault diagnosability analysis for linear discrete time systems. The main contribution lies in the design of the fault diagnosability evaluation indexes by combining the distance difference information with the direction difference information between the residuals in different cases. Under the assumption that the unknown inputs are random white noises, the residual generation is achieved by the parity space-based fault diagnosis approach. Based on this, the problem of fault diagnosability analysis is formulated as a bank of evaluation problems of the difference information between two residuals in fault-free case or in different faulty cases. Then, the fault diagnosability evaluation indexes are proposed based on the integrated design of the distance similarity function and the direction similarity function between different residuals, and the improved fault isolation conditions are constructed to provide an auxiliary index for fault diagnosability analysis. A simulation is carried out on a fixed-wing unmanned aerial vehicle flight control system, and the results demonstrate that the proposed method can achieve fault diagnosability analysis accurately for the linear discrete time systems.

Performance analysis of nonlinear observers applied to the fixed-wing unmanned aerial vehicles flight under decoupled-reduced model

In this paper, we present the design and implementation of two nonlinear observers: nonlinear extended state observer and sliding mode observer for estimating the pitch, yaw and roll angles and angular rates of a fixed-wing unmanned aerial vehicles system under a decoupled-reduced model in real flight experiments. A backstepping control law is designed for control in a decentralized way for altitude, yaw and roll of the airplane. This scheme allows us to test experimentally the feasibility of using the online estimated data from the observers in flight control, which is useful for increasing the robustness of the control and the safety of flight. Furthermore, a comparative analysis of the performance of both nonlinear observers is conducted.

Vision algorithms for fixed-wing unmanned aerial vehicle landing system

Autonomous landing has become a core technology of unmanned aerial vehicle (UAV) guidance, navigation and control system in recent years. As a novel autonomous landing approach, computer vision has been studied and applied in rotary-wing UAV landing successfully. This paper aims to fixed-wing UAV and focus on two problems: how to find runway only depending on airborne front-looking camera and how to align UAV with the designated landing runway. The paper can be divided into two parts to solve above two problems respectively. In the first part, the paper firstly presents an algorithm of region of interest (ROI) detection, which is based on spectral residual saliency map, and then an algorithm of feature vector extraction based on sparse coding and spatial pyramid matching (SPM) is proposed, finally, ROI including designated landing runway is recognized by a linear support vector machine. In the second part, the paper presents an approach of relative position and pose estimation between UAV and landing runway. Estimation algorithm firstly selects five feature points on the runway surface, and then establishes a new earth-fixed reference frame, finally uses orthogonal iteration to estimate landing parameters including three parameters of distance, height and offset, and three pose parameters of roll, yaw, pitch. The experimental results verify the effectiveness of the algorithms proposed in this paper.

Coordinated Path Planning for Fixed-Wing UAS Conducting Persistent Surveillance Missions

Algorithms for fixed-wing unmanned aerial systems (UAS) must integrate on-board sensor capabilities and vehicle maneuver constraints to reliably satisfy the objectives of persistent surveillance, path planning, and trajectory management. In many cases, the characteristic dimensions of sensor fields of view are comparable with the turning radius of the UAS platform. Consequently, when persistent, full area, time-critical coverage is required and the number of assets is limited, the complexity of path planning is increased, as the turn radius becomes comparable with or exceeds the sensor footprint. A technique is developed to integrate persistent surveillance mission requirements with sensor resolution and field-of-view to facilitate efficient path planning. Graph search techniques and spline-based methods are combined to develop computationally simple algorithms that converge to feasible paths with $G^{2}$ continuity. ( $G^{2}$ continuity in this context means the path, which is constructed from a concatenated series of $C^{2}$ curve segments, and its derivatives are continuous through two derivatives, but the magnitudes of second derivatives may differ at the boundaries, where these curve segments are joined to form the entire path. Curvature is continuous throughout.) Two example cases are provided. In the first, the turning radius is small in comparison with the sensor footprint, while in the second, the minimum turn radius is a critical parameter in the determination of a feasible path.